Computer/human generation, validation and use of a ground truth map to enforce data capture and transmission compliance in real and near real time video of a local scene

ABSTRACT

A hybrid computer/human method for generating, validating and using a ground truth map (GTM) provides for enforcement of data capture and transmission compliance of real and near real time video. Computer-implemented processes are used to identify and classify as allowed or disallowed objects in a local scene based on attributes of a video session. A human interface is available to validate either the object identification or classification. The GTM is then used, preferably in conjunction with motion sense, to enforce data capture and transmission compliance of real and near real time video within the local scene.

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates to the capture and processing of video to enforcedata capture and transmission compliance in real or near real-timeprivate, restrictive or secure environments.

Description of the Related Art

Video camera technology is increasingly ubiquitous in the world today.Devices such as head-mounted cameras, robotically controlled cameras,semi-autonomous or autonomous robots, cellular telephones, desktop ortable computers, near-eye displays and hand-held game systems, forexample, may include cameras and associated software to enable videocapture, display and transmission. These devices are being used toprovide unidirectional or bi-directional video communication in real ornear real time. Privacy and security concerns exists when, intentionallyor unintentionally, video that should not be captured, stored, displayedor transmitted is. These privacy and security issues are sometimescalled “data spills”. A person's, company's or country's privacy may beviolated, possibly illegally. In certain restrictive, such as militaryor company proprietary or secure environments, strict controls existgoverning what visual information may be captured, stored, displayed ortransmitted (particularly across country boundaries).

In an effort to restrict unwanted video capture or transmission, someexisting systems monitor the video as it is captured. These systems usehuman processing, computer vision (CV), artificial intelligence (AI),computational algorithms, or a combination thereof to identifyproblematic visual information (e.g. a person's face or a company'sproprietary information) and then either removes or obscures theinformation from the video file data. These systems may even shut offthe recording device to prevent further capture of problematicinformation. However, the existing systems described all capture, store,and process the problematic information. Because the problematic data isstored (albeit sometimes only temporarily) and processed, data spillrisks, such as those related to cyber incursions or theft, still exist,and therefore these systems cannot meet requirements for certain secureor restrictive environments. The processing required to remove orobscure the information from the video file makes these systemsincompatible with applications that require real or near real time videocapture and transmission.

Video capture that enforces data capture and transmission compliance inreal or near real-time may be needed in a variety of applications forindividual users, companies or countries. Such applications may includebut are not limited to inspection/process review, supplier qualitymanagement, internal audits, troubleshooting of equipment or systems,factory operations, factory collaboration, validation and verification,repair and upgrades of equipment, training on equipment or systems, ormedical procedures. In these applications, it may be necessary tocapture and uni-directionally or bi-directionally transmit video of alocal scene that includes problematic information in real or near realtime to facilitate efficient and effective remote communication. As aspecial case, data capture and transmission compliance may beimplemented in an Augmented Reality environment.

Augmented Reality (AR) refers to the generation of two or threedimensional (3D) video graphics or other media such that they areoverlaid on and registered with surrounding objects in the environment.Man-made “markers” aka “sources” having a unique and easily identifiablesignature may be placed on the user, on the object or in the scene andused for various purposes. These markers have been used to identify andlocate specific objects, to trigger the display of computer generatedmedia or to determine the position and pose of the user.

In certain video or AR environments such as a remote repair orinspection, a concern, primarily of the customer and which is heightenedby the video camera industry push to maximize the FOV, is that the userof the video being captured and transmitted or viewed locally (eitherthe field technician or expert, but primarily the field technician), mayturn away from the object of interest, intentionally or unintentionally,and capture video of another portion of the scene that should not becaptured or transmitted. Some level of data capture and transmissioncompliance may be required by customer demands, industry regulations,national security or country specific laws to avoid unintentional orintentional broad FOV transmission. Current techniques includephysically draping with a cloth or tarp the areas around the object ofinterest to prevent capture in the video signal, mechanically narrowingthe FOV, or sequestering the video prior to transmission and having asecurity-cleared domain expert review and edit the video signalpost-capture. These are often impractical and time consuming activities.Even more common, and more costly is the removal of the equipment inquestion to a specialized secure space, such as an empty garage orhanger so that there are no extraneous items in the scene. In many casesremoving equipment, physical draping or post-capture editing are eithernot sufficient to satisfy the compliance requirements or are impracticaland costly to implement in a quasi real-time interactive situation. Insome situations there are country laws that would prevent any type ofpost-capture editing for national security and ITAR—InternationalTraffic and Arms Regulations reasons.

U.S. Pat. Nos. 10,403,046 and 10,679,425 entitled “Field of View (FOV)and Key Code Limited Augmented Reality to Enforce Data Capture andTransmission Compliance” disclosed enforcement of an alignment conditionbetween the video camera's pointing direction and a marker in the sceneto avoid capture of excluded data in a real-time interactive situation.This may be done, for example, by determining whether the video camerapointing direction satisfies an alignment condition to a marker in thelocal scene such that the video camera FOV lies within a user-definedallowable FOV about the marker. A separate sensor may be used to detectthe presence of the marker within a sensor FOV to satisfy the alignmentcondition. The camera or sensor FOV may be reduced to create a bufferzone to provide additional assurance that the camera FOV does not strayoutside the allowable FOV. If the alignment condition is not satisfied,the video camera is controlled to exclude at least a portion of thecamera FOV that lies outside a user-defined allowable FOV from capturewithin the video signal. For example, this could be done by turning thevideo camera off or by narrowing its FOV. Markers may also be used as afail safe to ensure imagery in a particularly sensitive area of thescene is neither captured nor transmitted. If the separate sensordetects these markers, the video camera is shut down. The system may cuethe user e.g., “green” means the alignment condition is satisfied,“yellow” means the technician's eyes or camera are beginning to wanderand “red” means the alignment condition is violated and the camera isdisabled. In this system, the use of a separate sensor to enforce thealignement condition and to detect other markers in sensitive areas isspecifically designed for more rigorous environments, in whichcompliance requires that portions of the scene or tagged objects cannotbe captured (detected) by the video camera itself, much less output intothe video signal and for environments in which real or quasi real-timeinteraction is required.

SUMMARY OF THE INVENTION

The following is a summary of the invention in order to provide a basicunderstanding of some aspects of the invention. This summary is notintended to identify key or critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome concepts of the invention in a simplified form as a prelude to themore detailed description and the defining claims that are presentedlater.

The present invention provides a hybrid computer/human method forgenerating, validating and using a ground truth map (GTM) to provide forenforcement of data capture and transmission compliance of real and nearreal time video. Computer-implemented processes are used to identify andclassify as allowed or disallowed objects in a local scene based onattributes of a video session. A human interface is available tovalidate either the object identification or classification. The GTM isthen used to enforce data capture and transmission compliance of videowithin the local scene.

In an embodiment, the method comprises providing a library of objects inwhich each object is characterized by one or more attributes thatdetermine whether an object is allowed or disallowed. A GTM of the localscene is generated of the local scene including one or more identifiedobjects. The objects may be linked to models having defined attributesin the library. An interactive human interface is used to specify one ormore attributes including but not limited to object, task, environment,humans in the loop, transmission channel or security level. Acomputer-implemented process compares identified objects to the libraryand uses the specified attributes to classify the objects in the GTM asdisallowed or allowed. The interactive human interface displays the GTMand indicia of allowed and disallowed objects and preferably prompts theuser to confirm or override the object identifications orclassifications to output a final GTM. The user's ability to overrideobject classifications may be limited by the user's (or a supervisor's)authorization level. Any override and reasons for the override by may bestored in a historical record or used to modify attributes in thelibrary or the computer-implemented classification process. In certainconfigurations, the user (or supervisor) may be able to adjust or“throttle” the classification thresholds for object identification orclassification.

The method transitions to a video capture and transmission mode, inwhich a video camera captures a sequence of images within a camerafield-of-view (CFOV) in the local scene to form the video signal. Priorto forming the video signal, the final GTM is used to determine whetherthe CFOV will include disallowed or allowed objects. If a disallowedobject will be included in the CFOV, the video camera is controlled(e.g., turned off, CFOV narrowed or selected pixels blurred inreal-time) to prevent inclusion of the disallowed object in the videosignal. If no disallowed objects will be included in the CFOV, the videosignal is formed and transmitted.

In an embodiment, an initial GTM is generated in an objectidentification mode using a camera (same or different camera than thevideo camera) to create an image of the local scene, using acomputer-implemented process to identify the objects and using theinteractive human interface to confirm or override the objectidentifications. The initial GTM is then available for any specificvideo session in which the objects are classified as allowed ordisallowed based on the attributes of that session.

In a different embodiment, the interface is used to specify theattributes of a specific video session and then, as the camera is usedto create the image of the local scene, computer-implemented processesfirst identify and then classify the objects as allowed or disallowed.The interactive human interface is then used to confirm or override boththe object identifications and classifications.

In an embodiment, to use the final GTM in the video capture andtransmission mode, a pose (location and orientation) of the video camerais determined and used to extract a map FOV from the final GTM. Theprocess determines whether the map FOV includes disallowed or allowedobjects. In an alignment condition embodiment, the map FOV is used todetermine whether the video camera pointing direction satisfies analignment condition to a specified allowed object. In a time delayembodiment, the images are time-delayed prior to forming the videosignal to provide time to recognize disallowed objects in the map FOV.In a predicted FOV embodiment, motion of the camera is used to computepredicted map FOV to recognize disallowed objects. In addition, in thealignment condition and time-delay embodiments, object recognition maybe applied to the imagery in the camera FOV and compared to theidentified objects in the map FOV to validate that the same object isseen. In addition, the GTM can used to determine if a violation of thealignment condition or capturing a disallowed object is imminent andissue a cue to the user to take corrective action to avoid or mitigatethe violation.

In an embodiment, a motion sensor is configured to sense the motion ofany object before the object enters the video camera's CFOV. The movingobject might be an identified and classified object in the final GTM oran unidentified object that has moved into the local scene. The methodtreats any moving object as a disallowed and controls the video camerato prevent inclusion of the moving object in the video signal. In anembodiment, the video capture and transmission mode may be temporarilysuspended until the computer-implemented process can identify andclassify the object and the interactive human interface used to confirmor override the identification or classification before returning to thevideo capture and transmission mode.

In another embodiment, the method comprises providing a library ofobjects in which each object is characterized by one or more attributesthat determine whether an object is allowed or disallowed. A GTM of thelocal scene is generated including one or more identified objects. Acomputer-implemented process compares identified objects to the libraryof objects and uses attributes specified for a session to classify theobjects in a final GTM as disallowed or allowed. Foregoing the finaluser validation of the object classifications may be suitable when theapplication or particular video session makes such user validationimpracticable (e.g., time does not allow). Alternately, if theapplication has matured to a point that the attributes and computeridentification and classification algorithms are well established andvery accurate, the final user validation may not be required.

These and other features and advantages of the invention will beapparent to those skilled in the art from the following detaileddescription of preferred embodiments, taken together with theaccompanying drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a system in which a ground truth map (GTM)including allowed and disallowed objects is used to enforce data captureand transmission compliance of real and near real time video;

FIGS. 2A-2B are a block diagram of an embodiment of a GTM system and avideo capture, display and transmission (VCDT) device to enforce datacapture and transmission compliance of real and near real time video;

FIG. 3 is a screen shot of an embodiment of an attributes library;

FIG. 4 is an illustration of a map FOV based on the video camera's poseoverlaid on the final GTM;

FIGS. 5A-5E are screen shots of an embodiment of an interactive humaninterface for generating and validating a GTM;

FIGS. 6A-6C are screen shots of another embodiment of an interactivehuman interface for generating and validating a GTM;

FIG. 7 is an illustration of using the GTM to enforce an alignmentcondition to a specified object in the scene to enforce data capture andtransmission compliance;

FIG. 8 is an illustration of using the GTM with time-delayed capture ofthe video signal to enforce data capture and transmission compliance;and

FIG. 9 is an illustration of using the GTM with a predictive FOV of thevideo signal to enforce data capture and transmission compliance.

DETAILED DESCRIPTION OF THE INVENTION

Video capture that enforces data capture and transmission compliance inreal or near real-time may be needed in a variety of applications forindividual users, companies or countries. Such applications may includebut are not limited to inspection/process review, supplier qualitymanagement, internal audits, troubleshooting of equipment or systems,factory operations, factory collaboration, validation and verification,repair and upgrades of equipment, training on equipment or systems. Inthese applications, it may be necessary to capture and uni-directionallyor bi-directionally transmit video of a local scene that includesproblematic information in real or near real time to facilitateefficient and effective communication. As a special case, data captureand transmission compliance may be implemented in an Augmented Reality(AR) environment. The video camera pointing direction is slaved orcontrolled by user motion (e.g. a head mounted video camera or hand-heldvideo camera), a user-controlled manual manipulator (e.g. a robotic arm)or a fully automated manual manipulator (e.g. an AI controlled roboticarm or semi-autonomous or autonomous robot).

The present invention is directed to these and other similar applicationin which some level of data capture and transmission compliance may berequired by customer demands, industry regulations, national security orcountry specific laws. In certain instances, compliance may require thatportions of a scene or specifically tagged objects cannot be includedwithin the video signal output by the video camera for display ortransmission. In other more rigorous environments, compliance mayrequire that portions of the scene or tagged objects cannot be stored inthe camera's memory chip, much less output into the video signal. Thememory chip may be only a memory chip or may be a video display or videotransmission chip that includes the permanent memory. The required levelof compliance may be determined by a number of different factors and maychange between or even during capture and display or transmission of thevideo signal.

The present invention provides a hybrid computer/human method forgenerating, validating and using a ground truth map (GTM) for a videosession of a local scene to provide for enforcement of data capture andtransmission compliance of real and near real time video.Computer-implemented processes are used to identify and classify asallowed or disallowed objects in the local scene based on attributes ofthe video session. A human interface is available to validate either theobject identification or classification. The GTM is then used to enforcedata capture and transmission compliance of video within the localscene. This can be accomplished by computing a map FOV in the final GTMand enforcing an alignment condition to a specified allowed object,implementing an internal time-delay prior to formation of the videosignal, predicting the map FOV or a combination thereof.

Using the pose and motion of the video camera, cues can be derived fromthe final GTM and used to prompt a correction in the video camerapointing direction to prevent capture of a disallowed object before itoccurs or to enforce the alignment condition before it is lost. If thecues fail to cause corrective action, the video camera is thencontrolled to prevent capture of the disallowed objects or to punishloss of the alignment condition. As a result, disallowed objects do notmake it into the video signal and thus do not reach downstreamcircuitry, processing or a network to which the video camera may beconnected. This can be implemented in real or near real time or slowerif the application does not demand such performance with a delay line ortemporary memory chip positioned between the video camera's ROIC andmemory chip. For example, the slowest acceptable video frame rate formost users is approximately 24 frames/sec (fps), or approximately 42milliseconds (ms). A time-delay of less than 42 ms would be a generallyacceptable to most users. A fast video camera is 120 fps or about 8 ms.,which represents a refresh rate approximately 5 times faster thanacceptable human video viewing. At these frame rates, a delay of asingle frame is certainly real time. The predicted FOV can be utilizedto enforce data capture and transmission compliance for a single imageor a sequence of images in the video signal.

Without loss of generality, the invention will now be described in thecontext of an AR system in which a technician with a wearable AR deviceis performing some task on an object within a local scene. Video of theobject and local scene is captured and transmitted to a remote clientwho views the video and may provide guidance, instructions, data etc.back to the technician which are then overlaid on the video. It isunderstood that the invention can be used in any application in whichvideo is captured and transmitted to enforce compliance.

Referring now to FIG. 1 , in an embodiment an AR environment includes aGround Truth Map (GTM) system 10, a video capture, display andtransmission (VCDT) device 12 and a remote client 14 (e.g. a person at acomputer workstation with a camera) that enforce capture andtransmission compliant of real-time and near real-time video 16 ofobjects 18 in a local scene 20 over a transmission channel 22 to remoteclient 14. As depicted, the GTM system 10 and VDCT device 12 areseparate systems. In other embodiments, the GTM system 10 can beincorporated into the VDCT device 12, which may use a common videocamera or dedicated cameras/sensors.

GTM system 10 includes a sensor 24 (e.g. a video camera, a 3D camera,LIDAR, Sonar) configured to capture a sensed signal in a sensor FOV 26,which can be scanned, manually or automatically, to form an image of thelocal scene 20 that provides an initial GTM. A technician 30 uses aninteractive human interface 31 on a computer 32 to provide log-incredentials and other attributes of a video session. Acomputer-implemented object recognition algorithm processes the image toidentify the objects 18 as for example milling machine #1234 and engine#4321, which become part of the GTM 28. Computer-generated models may belinked to the objects as part of the GTM. The models may include or belinked to specific attributes. A computer-implemented objectclassification algorithm processes the objects and the attributes of thevideo session to classify each object as allowed “OK” or disallowed “X”to form an intermediate GTM.

What constitutes allowed or disallowed objects may depend on manydifferent attributes, which may be broken into attribute classes such asrelated to the object, task being performed during a video session,environmental, human, transmission channel, motion and security level.An object class may have specific attributes for the objectidentification, different classes to which the object might belong,different systems for which the object might be part of, differentsub-systems that might be part of the object, object shape, object size,material composition and so forth. The task class may specify apre-defined task such as inspection, repair, maintenance, replacementetc. or may be a description of the task. Task may also include adistance attribute whereby any object that is identified as having adistance from the video camera that is either too close (<min distance)or too far (>max distance) may be designated as disallowed. Theenvironmental class may include the country and specific site in whichthe local scene exists and the video is captured or the country andspecific site to which the video is transmitted. The human class mayinclude functions or authorization levels of the technicians thatcapture the video and perform the task, a supervisor with overrideauthority or the remote client. The transmission channel class mayinclude the one or more communication channels or networks through whichthe video must pass to reach the remote client. The motion class mayhave attributes that the rate of motion (e.g., velocity andacceleration) of the video camera or an object that enters the localscene may be defined as an object and disallowed if the rate of motionexceeds a maximum value. Depending upon the nature of the applicationthere can be many different specific attributes in each of theseenumerated classes as well as other types of attribute classes.

The attributes are suitably stored in a library of objects. The specificinstances of the attributes are defined at least in part through theinteractive human interface via the initial log-in or specifiedattributes for a given session. Other attributes may be provided throughother means such as the computer-generated models linked to specificobjects, auto-detection of the country of origin and site, etc.. Forexample, for a given video session a technician may provide his log-inkey codes that define his authorization/access levels, specify aparticular task to be performed on a specified object, specify thecountry and specific site of origin and the country and specific site ofdestination, a remote client key code, and an overall security level.The computer-implemented classification process maps these instances ofthe attributes against the library to initially determine whether eachobject in the local scene is allowed or disallowed.

This classification process can be performed in various ways. In oneapproach, the library is a multi-dimensional database in which eachattribute is associated with a dimension of the database. The databasestores either an allowed or disallowed value for the variouspermutations of the attributes. The classification algorithm simplyidentifies the corresponding value in the database. In another approach,each attribute is treated independently and is stored as allowed ordisallowed. The classification algorithm may simply take the union ofthe attributes and if any are disallowed then the object is disallowed.Alternately, the algorithm could give precedence to certain attributesto overrule other attributes or look for patterns within the attributes.In a different approach, the attributes are stored as descriptors ofsome type in which the classification algorithm implements artificialintelligence (AI) to process the different combinations or permutationsof the attributes to decide whether an object is allowed or disallowed.

The interactive human interface displays the intermediate GTM andindicia of allowed and disallowed objects on computer 32 and promptstechnician 30 to confirm or override the objects identifications or theallowed and disallowed object classifications to output a final GTM 34.In many applications, regardless of the sophistication or maturity ofthe computer-implemented processes to first identify and then classifythe objects as allowed or disallowed, it is still assumed that thejudgement of the human technician (or supervisor) is superior to andmore trusted than that of the computer. In many of these applications,the GTM cannot be released until the human technician has validated (orcorrected) the GTM. The final GTM 34 is passed to the VCDT device 12 viaa hard connection (e.g,. cable) or a soft connection (e.g., wireless). Ahardware of software firewall may be provided inside the VCDT device 12to ensure that neither the GTM, imagery of or data relating todisallowed objects gets transmitted.

The VCDT device 12 such as a pair of video goggles or a hand-held unit(e.g. a tablet or cell phone) has a pointing direction 40 that is slavedto technician motion (e.g., where a technician 42 is looking or pointingthe unit). VCDT device 12 includes a video camera 44 (e.g. a 2D or 3DCMOS, CCD or SWIR camera) configured to capture light within a cameraFOV (CFOV) 46 about pointing direction 40 to form the video signal 16 ofa portion of local scene 20. If the GTM system is integrated as part ofthe VCDT device 12, vocal commands or hand gestures could be used tointeract with the interface to provide attributes of the video sessionand to confirm or override object identifications or classifications.

In this example, field technician 42, which may or may not be the sameperson as technician 30, may be moving around inside a manufacturingfacility to confirm the existence and location of certain objects,repair or maintain certain objects or to use certain objects. Theseobjects may be considered to be “allowed” objects. The technician mayeven be prompted or cued to maintain the pointing direction 40 on aspecified object to perform a certain task (e.g., verification, repair,use). The field technician 42 can capture, display and transmit“allowed” objects. The field technician 42 cannot capture in memory 48,much less display or transmit, “disallowed” objects.

The VCTD device 12 cannot be activated to capture and transmit the videosignal 16 until a current and final GTM 34 for the video session isreceived from GTM system 10 and verified by the VCDT device via, forexample, a time stamp and session code. In other words, a techniciancannot just turn the VCTD device 12 to start taking and transmitting thevideo signal. The GTM system and VCTD device force a video session to bedefined (e.g., the attributes) and the computer and technician togenerate and validate the final GTM and send the final GTM to the VCTD.The set-up time for this process does cause a “pause” between theinitial definition of the video session and initiating the video sessionto capture and transmit compliant video in real or near real time.

Prior to forming the video signal 16, the VCDT device 12 uses the finalGTM 34 to determine whether CFOV 46 will include disallowed or allowedobjects. If a disallowed object will be included or an alignmentcondition to an allowed object is not satisfied, the video camera 44 iscontrolled (e.g., turned off, CFOV narrowed, pixels blurred pre-capture)to prevent inclusion of the disallowed object in video signal 16 or toenforce the alignment condition. If not, the video signal 16 is formedand transmitted over transmission channel 22 to the remote client 14. Tomake this determination, the VCDT device 12 measures a pose (locationand orientation) of the video camera, uses the pose to extract a map FOVfrom the final GTM 34 and determines whether a disallowed object isincluded in the map FOV to exclude disallowed objects or determines aline-of-sight (LOS) to a specified allowed object in the map FOV toenforce an alignment condition. Furthermore, the VCDT device can comparerecognized objects in the CFOV 46 to the ground truth map 34 to verifywhether it's the same object, allowed or disallowed and the location,which greatly improves the accuracy and confidence of the objectrecognition.

To prevent the capture and transmission of excluded data, varioustechniques including alignment condition, time-delay, predicted FOV or acombination thereof can be used to determine the presence of allowed ordisallowed objects in the map FOV in a timely manner as will bedescribed with the presentation of FIGS. 7, 8 and 9 , respectively.

Generally speaking, if a disallowed object is detected in the map FOV oran alignment condition to a specified allowed object in the map FOV isnot satisfied, the VCDT device 12 issues an interrupt 50 to control thevideo camera 44 to interrupt and stop images from being transferred intoa memory chip 48 where the video signal is formed. For example, if thepointing direction 40 satisfies an alignment condition (e.g., pointingdirection within a few degrees of a preferred line-of-sight (LOS)) toallowed object 18 to perform some task and do not include any disallowedobjects 18, the images captured by the video camera are transferred tomemory chip 48 where they are formed into the video signal that may bedisplayed to the field technician or transmitted to the remote client 14(e.g. storage or display to other remote users).

If both conditions are satisfied, the device may generate a positive cue(e.g. green “good”) to reinforce the technician's focus on the allowedobject. If the technician's pointing direction starts to wander awayfrom an allowed object or towards a disallowed object but has not yetviolated either condition, the device may generate a prompt cue (e.g.yellow “move left”) to take corrective action. If the technician'spointing direction has changed to the point that either the alignmentcondition is violated or capture of a disallowed object is imminent, thedevice may both control the video camera to prevent capture ofdisallowed objects and their inclusion in the video signal or deactivatethe camera and issue an interrupt cue (e.g. red “Deactivate VideoCamera”).

If either condition is violated, the device issues an “interrupt” 50that controls the camera to prevent capture of the video signalincluding disallowed objects or when the alignment condition is notsatisfied. For a violation of the alignment condition, the video camerais typically turned off either by interrupting power to the videocamera, deactivating the electrochemical top layer of the detector arrayor ROIC or by pointing the video camera in a completely differentdirection. For a violation of capturing a disallowed object, in additionto these options, the video camera may be controlled to optically narrowthe camera FOV or to selectively blur portions of the camera FOV (e.g.changing the f/ #), changing lighting of the local scene to causeblinding of the sensor, or selectively turn off or blur pixels on thedetector array corresponding to the disallowed object.

VCDT device 12 is suitably fitted with a motion sensor 60 that isconfigured to sense the motion of any object 62 before the object entersthe video camera's CFOV 46 (e.g. the motion sensor FOV 66 includes andextends beyond CFOV 46). The moving object might be an identified andclassified object in the final GTM or an unidentified object that hasmoved into the local scene. The VDCT device treats any moving object asa disallowed and controls the video camera 44 to prevent inclusion ofthe moving object in the video signal. In an embodiment, the videocapture and transmission mode may be temporarily suspended until thecomputer-implemented process can identify and classify the object andthe interactive human interface used to confirm or override theidentification or classification before returning to the video captureand transmission mode.

The same method can be applied to a remotely user-controlled robotic armthat points the video camera or a fully autonomous robot that uses avideo camera as part of its vision system. In the case of the roboticarm, “time-delay” can ensure that protected data is not captured andtransmitted to remote site, where the technician is located orelsewhere. In the case of a fully autonomous robot, “time-delay” canensure that protected data is not captured and used by the robot ortransmitted elsewhere.

The method can be applied to applications and local scenes in which onlyallowed objects are present (e.g., enforcing an alignment condition to aspecified allowed object) or only disallowed objects are present (e.g.,preventing capture and transmission of a disallowed object).

Referring now to FIGS. 2A-2B, 3 and 4 , in an embodiment similar to theone depicted in FIG. 1 , a GTM system 100 generates and validates afinal GTM 102 of a local scene for a particular video session and passthe final GTM 102 to a video capture, display and transmission (VCDT)device 200 that uses the final GTM 102 to enforce data capture andtransmission compliance on a real or near real-time video signal 202 inthe local scene.

GTM system 100 includes a sensor 104 such as a 2D camera, a 3D camera,LIDAR or sonar and optics 106 that capture a sensed signal within asensor FOV (SFOV) 108 along a pointing direction 110 and pass the sensedsignal to a GTM processor 112 to form a two or three-dimensional GTM102. An object recognition processor 114 processes the sensed signals toidentify objects in GTM 102. The objects may be linked tocomputer-generated models of the objects provided by digitalobjects/scene 116, which may also be used to provide a digital versionof the captured imagery or a partial digital scene to fill in gaps inthe sensed signals. An optional gyro 118 provides the GTM processor withpositional and orientation data to form the GTM 102 and position objectswithin the GTM.

An object library 120 includes attributes for a data base of knownobjects that together determine whether an object is allowed ordisallowed for a given video session as defined by particular instancesof a subset of those attributes. As previously described, the librarymay include object classes 122 possibly including but not limited to theobject itself, task, environment, human key codes, transmit channel andsecurity level. As shown in FIG. 3 , an object class 120 may haveseveral attributes.

An interactive human interface 130 is presented to the technician on adisplay 132. Interface 130 is configured to prompt and receive from atechnician a validation 134 (confirmation or override) of theidentification of each object. Interface 130 is configured to prompt andreceive from a technician attribute specification 136 to define aparticular video session (this may be done before or after validation ofthe object IDs). GTM processor 112 executes a classification algorithmto map the specified attributes against the library of attributes toclassify each object as allowed or disallowed to form an intermediateGTM. Interface 130 then prompts the technician to validate 134 (confirmor override) the classifications of each object (the validation of theIDs and classifications may be done concurrently) to output final GTM102.

In certain configurations, the human interface 130 may provide thetechnician (or supervisor) with the ability to adjust or “throttle” theclassification thresholds for object identification or classification.For example, if the object recognition processor 114 is misidentifyingtoo many objects, the technician may increase the threshold. Conversely,if the processor is leaving too many objects as unidentified, thetechnician may decrease the threshold. The classification algorithm maybe biased to initially classify objects as disallowed unless theattributes clearly indicate the object is allowable. If the processor ismis-classifying too many objects as disallowed, the technician mayreduce the classification threshold or bias.

VCDT device 200 is coupled to a “platform” 204 such as a user, roboticarm, robot etc. that controls the pointing direction of the device. VCDTdevice 200 includes a video camera 206 captures light within a camerafield-of-view (CFOV) 208 in pointing direction 210 in the local scene.The video camera suitably includes a power source 212, optics 214 tocollect light within the CFOV, a detector array 216 to sense andintegrate light to form an image converting photons to electrons, a readout integrated circuit (ROIC) 218, which includes an amplifier and anA/D converter, to read out a sequence of images at a frame rate, atime-delay element 220 and a memory chip 222 to store the sequence ofimages and pass them to a video processor 224 to form the video signal202 for a display 226 or transmission.

The VCTD device 100 cannot be activated to capture and transmit thevideo signal 202 until a current and final GTM 102 for the video sessionis received from GTM system 200 and verified by the VCDT device via, forexample, a time stamp and session code. In other words, a techniciancannot just turn the VCTD device 100 to start taking and transmittingthe video signal. The GTM system and VCTD device force a video sessionto be defined (e.g., the attributes) and the computer and technician togenerate and validate the final GTM and send the final GTM to the VCTD.The set-up time for this process does cause a “pause” between theinitial definition of the video session and initiating the video sessionto capture and transmit compliant video in real or near real time.

An interrupt processor 230 controls video camera 206 to preventdisallowed objects 232 from being captured in the video signal or toenforce an alignment condition to a specified allowed object 234.Interrupt processor 230 receives the final GTM 102 and determines a pose236 including a location and an orientation of the video camera todetermine a map FOV 238. This may be done either thru use of a gyroscope240 that measures the 6 DOF pose (e.g. x,y,z and rotation about x,y,zaxes) of the video camera or by matching the image in the CFOV for thecurrent frame against ground truth map 103. Prior to receiving andverifying the final GTM 102, the interrupt processor 230 may issue aninterrupt (e.g., turn video camera 206 off) that prevents the videocamera from capturing and transmitting any video.

Interrupt processor 230 determines whether the map FOV 238 includes aspecified allowed object 234 or a disallowed object 232. As furtherillustrated in FIGS. 7, 8 and 9 the interrupt processor 230 can usealignment condition, time-delay and predicted FOV techniques or acombination thereof to determine whether the map FOV 238 includes adisallowed object 232 or whether the orientation of the video camerasatisfies an alignment condition to the specified allowed object 234sufficient to exclude the disallowed object 232 from the CFOV in amanner that prevents inclusion of disallowed objects in real ornear-real time video for transmission. In the alignment conditiontechnique, the interrupt processor enforces an alignment of the videocamera orientation or pointing direction to the specified allowedobject. In the time-delay technique, the captured frames are delayed bytime-delay element 220 giving the interrupt processor sufficient time toprocess the frames to make the determination. In the predicted FOVtechnique, a motion sensor 240 proves measurement of velocity andacceleration, suitably in 6 DOF, that when combined with the currentpose 236 provided predicted map FOV for one or more future frames fromwhich the interrupt processor can make the determination.

If the interrupt processor 230 determines that either the map FOV, hencethe video camera CFOV will include a disallowed object or does notsatisfy an alignment condition, the processor issues an interrupt 242 tocontrol the video to prevent capture of the disallowed object and itsinclusion in the video signal. If an interrupt is issued, the videosignal 202 may receive no video signal if power was interrupted, mayreceive a blank or noisy video signal if the ROIC is deactivated or mayreceive a video signal in which the pixels corresponding to thedisallowed objected are removed or obscured.

Interrupt processor 230 may generate a cue 244 to change the videocamera pointing direction to prevent capture of disallowed objects andtheir inclusion in the one or more future frames of the video signalwithout having to control or turn-off the video camera. Cue 244 isconfigured to preempt movement of the video camera towards thedisallowed object before it occurs. For example, if the alignmentcondition is met a “green” cue may be displayed, if the alignment startsto wander a “yellow” cue is displayed and if the alignment fails a “red”cue is displayed. After generating the cue, the interrupt processorupdates the one or more predicted FOV to determine whether the updatedpredicted FOV includes the disallowed object. If the cue fails toprevent capture of the disallowed object in the updated predicted FOV,then the interrupt processor 230 issues the interrupt 242 to control thevideo to prevent capture of the disallowed object and its inclusion inthe video signal.

If the video camera is trained on allowed object 234 and away fromdisallowed objects 1232, the interrupt processor 240 determines whetherthe camera's pointing direction satisfies an alignment condition to oneof the allowed objects. If not, the system generates a cue 244 to changethe video camera pointing direction to enforce the alignment condition.If the cue fails to enforce the alignment condition, the video camera isdeactivated. Loss of the alignment condition does not necessarily meanthat the camera is going to capture a disallowed object. However, if thevideo camera wanders off of the allowed object and the cue fails tocorrect the problem, turning off the video camera, at least temporarily,is effective to train the platform to maintain the proper alignment tothe allowed object to perform the task at hand. The length of time thevideo camera is turned off can vary in order to more effectively trainthe local or remote user or robot.

As image frames are generated by the ROIC 218, the frames can be passedto an object recognition processor 246 configured to recognize andidentify any objects in the frame. Interrupt processor 230 compares therecognized objects in the CFOV 208 to the ground truth map 102 to verifywhether it's the same object, allowed or disallowed and the location,which greatly improves the accuracy and confidence of the objectrecognition.

VCDT device 200 is suitably fitted with a motion sensor 248 that isconfigured to sense the motion of any object before the object entersthe video camera's CFOV 208 (e.g. the motion sensor FOV 250 includes andextends beyond CFOV 208). The moving object might be an identified andclassified object in the final GTM or an unidentified object that hasmoved into the local scene. The VDCT device treats any moving object asa disallowed and controls the video camera 44 to prevent inclusion ofthe moving object in the video signal. In an embodiment, the videocapture and transmission mode may be temporarily suspended until thecomputer-implemented process can identify and classify the object andthe interactive human interface used to confirm or override theidentification or classification before returning to the video captureand transmission mode.

Referring now to FIGS. 5A-5E that depict a sequence of screen shots ofthe interactive human interface, in an embodiment for generating andvalidating a final GTM, as shown in FIG. 5A an initial GTM 300 suitablyincludes real imagery 302 of a local scene including one or more objects304 and 306, which have been identified and suitably linked tocomputer-generated models of the particular object. A technician selects“Confirm ID” from the menu. As shown in FIG. 5B, the interface displaysa prompt 308 to prompt the technician to confirm or override each objectidentification. If an override is made, the interface displays a prompt310 to prompt the technician to enter the correct identification and anyother relevant information, which can be stored in a historical recordand used to modify the object databases or object recognitionalgorithms. Once each of the objects has been validated, the technicianselects “Attributes” from the menu. As shown in FIG. 5C, the interfacedisplays a menu 312 of attributes 314 such as object, task, environment,humans, security level etc. and prompts the technician to select orotherwise provide a specific instance for each attribute which togetherdefine (at least in part) the video session. Some of the attributes maybe populated via the log-in key codes, automatic detection of thecountry or origin, etc. Once the specification of attributes for thevideo session is complete, the technician can select “New GTM” to runthe computer-implemented process to match the specified attributes tothe library of attributes to classify each object as allowed ordisallowed and to display indicia 316 (e.g., words allowed ordisallowed, a green check or a red X) as shown in FIG. 5D. To completethe GTM, the technician selects “Review” and as shown in FIG. 5E theinterface prompts the technician to confirm (“Go” prompt 318) or tooverride (“Edit” prompt 320) each object classification. Again, if thetechnician overrides a classification, the technician is prompted toenter the reasons for the override, which are then stored in ahistorical record or used to modify the attribute library orclassification algorithm. In this embodiment, the initial GTM isvalidated as to object identification and then available for anyspecific video session in which the objects are classified based on theattributes for that session.

In a different embodiment, the interface is used to specify theattributes of a specific video session and then, as the camera/sensor isused to create the image of the local scene (possibly augmented withcomputer-models of the objects), computer-implemented processes firstidentify and then classify the objects as allowed or disallowed. Theinteractive human interface is then used to confirm or override both theobject identifications and classifications. As shown in FIG. 6A, theinterface first displays a menu 600 of attributes 602 for technicianselection/definition to provide the attributes that define a videosession. Once complete, the technician selects “GTM”, the camera/sensorand computer-implemented processes generate a GTM 604 including imagery606 of the local scene and identified and classified objects 608 and610. Indicia 612 and 614 of the object identification andclassification, respectively, are displayed as shown in FIG. 6B. Tocomplete the GTM, the technician selects “Review” and as shown in FIG.5E the interface prompts the technician to confirm (“Go” prompt 616) orto override (“Edit” prompt 618) each object classification. Again, ifthe technician overrides a classification, the technician is prompted toenter the reasons for the override, which are then stored in ahistorical record or used to modify the attribute library orclassification algorithm.

As previously mentioned, to prevent the capture and transmission ofexcluded data in real or near real-time, various techniques includingalignment condition, time-delay, predicted FOV or a combination thereofcan be used to determine the presence of allowed or disallowed objectsin the map FOV in a timely manner.

Referring now to FIG. 7 , an “Alignment Condition” technique ensures inreal or near real-time that disallowed objects 700 are not included inthe video stream by enforcing an alignment condition 702 to a specifiedallowed object 704. The alignment condition can be set to plus/minus afew degrees to ensure the technician 706 keeps his/her eyes on thespecified object or the interrupt processor, knowing the location of thetechnician (video camera 708) can determine from the final GTM 710 howmuch the technician's eyes (camera pointing direction 712) can wanderfrom a specified object before a disallowed object will appear in thecamera FOV (CFOV) 714. In the latter case, the alignment condition maychange as technician moves to perform a given task. The alignmentcondition can be enforced either by monitoring changes in thetechnician's pose (via the gyro) or by comparing a map FOV 716 for thecurrent frame against final GTM 710. The interrupt process may alsogenerate cues that are displayed (or verbalized) to the technician. Ifthe camera pointing direction 712 is pointing directly at allowableobject 704 a green “good” might be provide. If the pointing direction712 starts to wander but is still withing the alignment condition 702 ayellow-correct right might be provided. If the pointing direction 712violates the alignment condition 702 a red-camera deactivated might beprovided and an interrupt issued to the video camera. Additional detailsfor enforcement of alignment condition without a GTM are provided inU.S. Pat. Nos. 10,403,046 and 10,679,425.

Referring now to FIG. 8 , a “Time-Delay” technique ensures in real ornear real-time that disallowed objects 800 are not included in the videostream 802 by inserting a time-delay 804 between the sequence of images806 read out by the ROIC and the storage of the sequence of images inthe memory chip to form the video stream. The time-delay 804 must belong enough that a map FOV 808 in a final GTM 810 can be processed todetect the presence of a disallowed object 812 (or to ensure analignment condition is met to an allowed object 814) and issue aninterrupt 816 before the problematic image is included in the videostream 802 and short enough that capture and transmission of the videostream 802 is still real or near real time. In another embodiment, asecond sensor having a FOV that spans the entire CFOV can be used inconjunction with the final GTM to recognize objects and issue theinterrupt. The interrupt processor may generate cues for the technician.Additional details for capture and transmission compliance usingtime-delay are providing in co-pending U.S. application Ser. No.17/507,073 entitled “Time-Delay to Enforce Data Capture and TransmissionCompliance in Real and Near Real Time Video” filed Oct. 21, 2021.

Referring now to FIG. 9 , a “Predictive FOV” technique ensures in realor near real-time that disallowed objects 900 are not included in thevideo stream by using a current CFOV 902 (map FOV 903) and camera pose904 and measurements of camera velocity and acceleration 906 to computepredicted CFOV 908 and predicted map FOV 910 in the final GTM 912 forone or more future frames. The interrupt processor determines whetherany of the future frames contain a disallowed object 900 or fail tosatisfy an alignment condition to an allowed object 914. The interruptprocessor may also generate cues that are displayed (or verbalized) tothe technician. If the camera pointing direction is pointing directly atallowable object 914 a green “good” 916 might be provide. If thepointing direction starts to wander in the predicted CFOV 908 but isstill withing the alignment condition a yellow-correct right 918 mightbe provided. If the pointing direction in the predicted CFOV 908violates the alignment condition a red-deactivate camera 920 might beprovided and an interrupt issued to the video camera. Additional detailsfor capture and transmission compliance using time-delay are providingin co-pending U.S. application Ser. No. 17/507,073 entitled “PredictiveField-of-view (FOV) and Cueing to Enforce Data Capture and TransmissionCompliance in Real and Near Real Time Video” filed Oct. 21, 2021.

While several illustrative embodiments of the invention have been shownand described, numerous variations and alternate embodiments will occurto those skilled in the art. Such variations and alternate embodimentsare contemplated, and can be made without departing from the spirit andscope of the invention as defined in the appended claims.

We claim:
 1. A method of preventing capture and transmission of excludeddata in a local scene from a video signal, said method comprising:providing a library of objects, each object characterized by one or moreattributes that determine whether an object is allowed or disallowed;generating a ground truth map of the local scene including one or moreidentified objects; using an interactive human interface to specify oneor more attributes; using a computer-implemented process to compareidentified objects to the library of objects and use the specifiedattributes to classify the objects in the ground truth map as disallowedor allowed; and using the interactive human interface to display theground truth map and indicia of allowed and disallowed objects and toreceive human input to confirm or override the allowed and disallowedobject classifications to output a final ground truth map; in a videocapture and transmission mode, using a video camera at a position in thelocal scene to capture a sequence of images within a camerafield-of-view (CFOV) in a pointing direction in the local scene to formthe video signal; prior to forming the video signal, using the finalground truth map to determine whether the CFOV will include disallowedor allowed objects; if a disallowed object will be included in the CFOV,controlling the video camera to prevent inclusion of the disallowedobject in the video signal; and if no disallowed objects will beincluded in the CFOV, forming and transmitting the video signal.
 2. Themethod of claim 1, in a object identification mode, generating theground truth map comprises using a camera to create an image of thelocal scene; using a computer-implemented process to identify objects inthe image; and using the interactive human interface to display theimage and indicia of the identified objects and to receive human inputto confirm or override the object identifications, and then in an objectvalidation mode, using the interactive human interface to specify theattributes, the computer-implemented process to classify the objects andthen the interactive human interface to confirm or override theclassifications.
 3. The method of claim 1, wherein in an objectidentification and validation mode, using the interactive humaninterface to specify the attributes, as a camera is used to create animage of the local scene, using a computer-implemented process to firstidentify and then classify objects as allowed or disallowed; and thenusing the interactive human interface to display the image and indiciaof the identified and classified objects and to receive human input toconfirm or override both the object identifications and classifications.4. The method of claim 1, further comprising using the interactive humaninterface to vary a classification threshold of either thecomputer-implemented process to identify objects or thecomputer-implemented process to classify the objects as allowed ordisallowed.
 5. The method of claim 1, wherein a step of generating theground truth map comprises: linking computer models of the objects tothe identified object in the ground truth map and to the attributes inthe library.
 6. The method of claim 1, wherein the specified attributesinclude a plurality of object, environment, task, human, transmissionchannel and security level attributes.
 7. The method of claim 6, whereinthe interactive human interface displays a prompt to confirm or overrideeach of the allowed and disallowed object classifications and requires aconfirmation or override of each object classification to output thefinal ground truth map.
 8. The method of claim 1, wherein the ability ofhuman input to override the allowed and disallowed objectclassifications is defined by attributes of the operator or a superioror attributes of the classified object.
 9. The method of claim 1,wherein if human input overrides an object classification, theinteractive human interface displays a prompt to enter reasons for theoverride, further comprising: storing the reasons for the override in ahistorical record; or using the reasons for the override to modify theattributes of the object in the library or to modify thecomputer-implemented process to classify the objects.
 10. The method ofclaim 1, wherein in the video capture and transmission mode, a step ofusing the final ground truth map to determine whether the CFOV willinclude disallowed or allowed objects includes determining a poseincluding a location and an orientation of the video camera within thelocal scene; using the pose to extract a map FOV from the final groundtruth map; and determining whether the map FOV includes disallowed orallowed objects.
 11. The method of claim 10, further comprising:comparing the imagery within the camera FOV to the map FOV to confirmwhether the map FOV includes disallowed or allowed objects.
 12. Themethod of claim 10, wherein the step of using the final ground truth mapto determine whether the CFOV will include disallowed or allowed objectsfurther comprises; determining whether the video camera pointingdirection satisfies an alignment condition to a specified allowed objectin the map FOV to exclude any disallowed objects from the CFOV.
 13. Themethod of claim 10, wherein the step of using the final ground truth mapto determine whether the CFOV will include disallowed or allowed objectsfurther comprises: delaying the sequence of images by a time-delay priorto formation of the video signal; and in response to recognition of adisallowed object in the map FOV and prior to expiration of thetime-delay, controlling the video camera to prevent storage of imagesincluding the disallowed object and its inclusion in the video signal.14. The method of claim 10, wherein a step of using the final groundtruth map to determine whether the CFOV will include disallowed orallowed objects further comprises; receiving measurements of velocityand acceleration of the video camera's pointing direction; computing oneor more predicted map FOV for one or more future frames from the poseand the measurements of velocity and acceleration; and comparing the oneor more predicted map FOV to the final ground truth map to recognize andlocate disallowed objects.
 15. The method of claim 10, generating a cueto change the video camera pointing direction to prevent capture ofdisallowed objects and their inclusion in the one or more future framesof the video signal; after generating the cue, updating the map FOV todetermine whether the updated map FOV includes the disallowed object;and if the cue fails to prevent capture of the disallowed object in theupdated predicted map FOV, then controlling the video camera to preventcapture of the disallowed object and its inclusion in the video signal.16. The method of claim 1, wherein in the video capture and transmissionmode, further comprising: sensing motion of any object before the objectenters the video camera's CFOV and treating any moving object as adisallowed object such that the video camera is controlled to preventinclusion of the moving object in the video signal.
 17. The method ofclaim 16, if a moving object is sensed, temporarily suspending the videocapture and transmission mode; using the computer-implemented process toidentify and classify the moving object as allowed or disallowed; usingthe interactive human interface to display the ground truth map andindicia of the classification of the moving object to confirm oroverride the classification; and returning to the video capture andtransmission mode.
 18. A method of preventing capture and transmissionof excluded data in a local scene from a video signal, said methodcomprising: providing a library of objects, each object characterized byone or more attributes that determine whether an object is allowed ordisallowed; generating a ground truth map of the local scene includingone or more identified objects; and using a computer-implemented processto compare identified objects to the library of objects and useattributes specified for a particular session to classify the objects ina final ground truth map as disallowed or allowed; in a video captureand transmission mode, using a video camera at a position in the localscene to capture a sequence of images within a camera field-of-view(CFOV) in a pointing direction in the local scene to form the videosignal; prior to forming the video signal, using the final ground truthmap to determine whether the CFOV will include disallowed or allowedobjects; if a disallowed object will be included in the CFOV,controlling the video camera to prevent inclusion of the disallowedobject in the video signal; and if no disallowed objects will beincluded in the CFOV, forming and transmitting the video signal.
 19. Themethod of claim 18, wherein the step of generating the ground truth mapcomprises: using a camera to create an image of the local scene; using acomputer-implemented process to identify objects in the image; and usingthe interactive human interface to display the image and indicia of theidentified objects and to receive human input to confirm or override theobject identifications.
 20. The method of claim 18, wherein the step ofgenerating the ground truth map comprises: linking computer models ofthe objects to the identified object in the ground truth map and to theattributes in the library.
 21. The method of claim 18, wherein in thevideo capture and transmission mode, further comprising: sensing motionof any object before the object enters the video camera's CFOV andtreating any moving object as a disallowed object such that the videocamera is controlled to prevent inclusion of the moving object in thevideo signal.
 22. A method of preventing capture and transmission ofexcluded data in a local scene from a video signal, said methodcomprising: generating a ground truth map of the local scene includingone or more identified objects; and classifying the objects in theground truth map as disallowed or allowed; in a video capture andtransmission mode, using a video camera at a position in the local sceneto capture a sequence of images within a camera field-of-view (CFOV) ina pointing direction in the local scene to form the video signal; priorto forming the video signal, using the ground truth map to determinewhether the CFOV will include a disallowed object; if a disallowedobject will be included in the CFOV, controlling the video camera toprevent inclusion of the disallowed object in the video signal; and ifno disallowed objects will be included in the CFOV, forming andtransmitting the video signal.
 23. The method of claim 22, wherein inthe video capture and transmission mode, further comprising: sensingmotion of any object before the object enters the video camera's CFOVand treating any moving object as a disallowed object such that thevideo camera is controlled to prevent inclusion of the moving object inthe video signal.